SigSegment: A Signal-Based Segmentation Algorithm for Identifying Anomalous Driving Behaviours in Naturalistic Driving Videos

In recent years, distracted driving has garnered considerable attention as it continues to pose a significant threat to public safety on the roads. This has increased the need for innovative solutions that can identify and eliminate distracted driving behavior before it results in fatal accidents. In this paper, we propose a Signal-Based anomaly detection algorithm that segments videos into anomalies and non-anomalies using a deep CNN-LSTM classifier to precisely estimate the start and end times of an anomalous driving event. In the phase of anomaly detection and analysis, driver pose background estimation, mask extraction, and signal activity spikes are utilized. A Deep CNN-LSTM classifier was applied to candidate anomalies to detect and classify final anomalies. The proposed method achieved an overlap score of 0.5424 and ranked 9th on the public leader board in the AI City Challenge 2023, according to experimental validation results

Effects of Exit Doors and Number of Passengers on Airport Evacuation Effeciency Using Agent Based Simulation

Many factors determine the efficiency of evacuation at an airport during emergencies. These factors are very complicated and many times, unpredictable. The Federal Aviation Administration provides numerous advisory circulars and regulations for managing airport evacuation. However, a thorough literature review suggests that research on airport evacuation is still very limited. A study was designed to simulate an airport evacuation to address this problem. This study selected a local certificated airport in the United States for this purpose. We developed and validated a situation model using AnyLogic to investigate evacuation time at this airport. Using different variables, such as the number of passengers and the number of exits, we calculated the total evacuation time. As a result, this study provided statistical data to show how the reduced number of exits and the increased amount of passenger traffic increased the total duration of the evacuation

Multi-scale Models for Transportation Systems Under Emergency Conditions

The purpose of this study is to investigate human behavior in emergencies. More specifically, agent-based simulation and social force models were developed to examine the impact of various human and environmental factors on the efficiency of the evacuation process, through a series of case studies. The independent variables of the case studies include the number of exits, the number of passengers, the evacuation policies, and instructions, as well as the queue configuration and wall separators. The results revealed the location of the exits, number of exits, evacuation strategies, and group behaviors all significantly impact the total time of the evacuation. For the queue configuration, short aisles lower infection spread when rope separators were used. The findings provide new insights in designing layout, planning, practice, and training strategies for improving the effectiveness of the pedestrian evacuation process under emergency

Robotic Non-destructive Test of Concrete Structures with GPR, Impact Echo and 3D Vision

International audienceReinforced concrete (RC) structures need to be frequently inspected. The visual inspection is the practice in the structural engineering field. In the case where the structures are not accessible, using RealSense Camera mounted on a robot plays a vital role to detect external defects of RC members. In this paper, a RealSense Camera is used to inspect two different reinforced concrete beams built with specified surface defects. The First set is control beams by having mostly smooth surface with a small honeycombing area. The second set is RC beams that have excessive honeycombing defects in almost throughout the RC beam's side. Intel RealSense D435 Depth Camera is employed to scan the side of beams and record the data in X, Y and Z (depth) directions while camera is moving on a robot. Also, the MATLAB toolbox is used to convert the matrix data into image processing technique and the Mesh Plot is exploited to capture the images. The results show that the camera's images accurately depict the surface damaged areas and provide accurate representation of depths of the surface indentations

Travel Time, Distance and Costs Optimization for Paratransit Operations using Graph Convolutional Neural Network

The provision of paratransit services is one option to meet the transportation needs of Vulnerable Road Users (VRUs). Like any other means of transportation, paratransit has obstacles such as high operational costs and longer trip times. As a result, customers are dissatisfied, and paratransit operators have a low approval rating. Researchers have undertaken various studies over the years to better understand the travel behaviors of paratransit customers and how they are operated. According to the findings of these researches, paratransit operators confront the challenge of determining the optimal route for their trips in order to save travel time. Depending on the nature of the challenge, most research used different optimization techniques to solve these routing problems. As a result, the goal of this study is to use Graph Convolutional Neural Networks (GCNs) to assist paratransit operators in researching various operational scenarios in a strategic setting in order to optimize routing, minimize operating costs and minimize their users' travel time. The study was carried out by using a randomized simulated dataset to help determine the decision to make in terms of fleet composition and capacity under different situations. For the various scenarios investigated, the GCN assisted in determining the minimum optimal gap

Effects of Exit Doors and Number of Passengers on Airport Evacuation Effeciency Using Agent Based Simulation

Many factors determine the efficiency of evacuation at an airport during emergencies. These factors are very complicated and many times, unpredictable. The Federal Aviation Administration provides numerous advisory circulars and regulations for managing airport evacuation. However, a thorough literature review suggests that research on airport evacuation is still very limited. A study was designed to simulate an airport evacuation to address this problem. This study selected a local certificated airport in the United States for this purpose. We developed and validated a situation model using AnyLogic to investigate evacuation time at this airport. Using different variables, such as the number of passengers and the number of exits, we calculated the total evacuation time. As a result, this study provided statistical data to show how the reduced number of exits and the increased amount of passenger traffic increased the total duration of the evacuation

Novel Highly Flexible PCB Design Based on a Via-Less Meander Ground Structure to Transmit mm-Wave RF Signals in 5G Foldable Mobile Products

Recently, new form factors, such as foldable, have increased demand for mobile products.Moreover, mobile phones should support the RF signal frequency up to the mm-wave frequencydue to the expansion of 5G mobile products. Therefore, 5G foldable products require componentsthat facilitate both mm-wave RF transmission and ultra-high flexibility for interconnecting throughthe hinge structure of foldable products. To improve flexibility, a flexible PCB must be thin with noground vias in its bending section; in contrast, the low-loss flexible PCB for mm-wave transmissionmust be thick and have many ground vias, so there is a trade-off relationship between flexibilityand RF characteristics. This paper proposes a new flexible PCB structure that does not experienceproblems regarding signal transmission to the mm-wave band, even when folded 200,000 times.To overcome the physical limits of the trade-off relationship, an interlayer air-gap was formed; astructure with a via-less and meander ground shape is proposed. The simulated loss of the proposedstructure was 0.0254 dB/mm @ 10 GHz, and the isolation between signals ranged from 21.98 dBto 10 GHz. The simulated results of insertion loss and isolation were experimentally verified. Theproposed structure is currently being applied to the RF flexible PCB that interconnects through thehinge of a foldable phone, and is currently being mass-produced

Antiviral Efficacies of FDA-Approved Drugs against SARS-CoV-2 Infection in Ferrets

The SARS-CoV-2 pandemic continues to spread worldwide, with rapidly increasing numbers of mortalities, placing increasing strain on health care systems. Despite serious public health concerns, no effective vaccines or therapeutics have been approved by regulatory agencies. In this study, we tested the FDA-approved drugs lopinavir-ritonavir, hydroxychloroquine sulfate, and emtricitabine-tenofovir against SARS-CoV-2 infection in a highly susceptible ferret infection model. While most of the drug treatments marginally reduced clinical symptoms, they did not reduce virus titers, with the exception of emtricitabine-tenofovir treatment, which led to diminished virus titers in nasal washes at 8 dpi. Further, the azathioprine-treated immunosuppressed ferrets showed delayed virus clearance and low SN titers, resulting in a prolonged infection. As several FDA-approved or repurposed drugs are being tested as antiviral candidates at clinics without sufficient information, rapid preclinical animal studies should proceed to identify therapeutic drug candidates with strong antiviral potential and high safety prior to a human efficacy trial.Due to the urgent need of a therapeutic treatment for coronavirus (CoV) disease 2019 (COVID-19) patients, a number of FDA-approved/repurposed drugs have been suggested as antiviral candidates at clinics, without sufficient information. Furthermore, there have been extensive debates over antiviral candidates for their effectiveness and safety against severe acute respiratory syndrome CoV 2 (SARS-CoV-2), suggesting that rapid preclinical animal studies are required to identify potential antiviral candidates for human trials. To this end, the antiviral efficacies of lopinavir-ritonavir, hydroxychloroquine sulfate, and emtricitabine-tenofovir for SARS-CoV-2 infection were assessed in the ferret infection model. While the lopinavir-ritonavir-, hydroxychloroquine sulfate-, or emtricitabine-tenofovir-treated group exhibited lower overall clinical scores than the phosphate-buffered saline (PBS)-treated control group, the virus titers in nasal washes, stool specimens, and respiratory tissues were similar between all three antiviral-candidate-treated groups and the PBS-treated control group. Only the emtricitabine-tenofovir-treated group showed lower virus titers in nasal washes at 8 days postinfection (dpi) than the PBS-treated control group. To further explore the effect of immune suppression on viral infection and clinical outcome, ferrets were treated with azathioprine, an immunosuppressive drug. Compared to the PBS-treated control group, azathioprine-immunosuppressed ferrets exhibited a longer period of clinical illness, higher virus titers in nasal turbinate, delayed virus clearance, and significantly lower serum neutralization (SN) antibody titers. Taken together, all antiviral drugs tested marginally reduced the overall clinical scores of infected ferrets but did not significantly affect in vivo virus titers. Despite the potential discrepancy of drug efficacies between animals and humans, these preclinical ferret data should be highly informative to future therapeutic treatment of COVID-19 patients